Systems, apparatuses, and methods for document querying

ABSTRACT

Techniques for searching documents are described. An exemplary method includes receiving a document search query; querying at least one index based upon the document search query to identify matching data; fetching the identified matched data; determining one or more of a top ranked passage and top ranked documents from the set of documents based upon one or more invocations of one or more machine learning models based at least on the fetched identified matched data and the document search query; and returning one or more of the top ranked passage and the proper subset of documents.

BACKGROUND

Enterprises are generating more data than ever before. Trying to findwhat data is relevant from that generated data is a non-trivial task.Traditional search solutions rely on keyword-based document analysis tofind specific terms in the data which a general-purpose approachinherently limited by its inability to “understand” the content at amore granular level.

BRIEF DESCRIPTION OF DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates embodiments of an enterprise search service.

FIG. 2 illustrates embodiments of the enterprise search service used forproviding inference functionality.

FIG. 3 illustrates embodiments of the enterprise search service used forproviding inference functionality.

FIG. 4 illustrates embodiments of a method for performing an inference(search on documents).

FIG. 5 illustrates embodiments of an improved display of a result of aninference query.

FIG. 6 illustrates embodiments of a method for performing an improveddisplay of a result of an inference query.

FIG. 7 illustrates embodiments of the enterprise search service 102 usedfor providing ingestion functionality.

FIG. 8 illustrates embodiments of a method for performing ingestion ofone or more documents.

FIG. 9 illustrates embodiments of exemplary reserved fields for use iningestion.

FIG. 10 illustrates embodiments of a graphical user interface to be usedin updating/adding/removing reserved fields for use in ingestion.

FIG. 11 illustrates embodiments of a model building system.

FIG. 12 illustrates embodiments of a method for model management.

FIG. 13 illustrates embodiments of a graphical user interface to be usedin active learning of question and answer(s) for training a machinelearning model.

FIG. 14 illustrates embodiments of a graphical user interface to be usedin active learning of document ranking for training a machine learningmodel.

FIG. 15 illustrates embodiments of a method for active learning fortraining a machine learning model.

FIG. 16 illustrates embodiments of a method for training and use of aquestion generation model.

FIG. 17 illustrates a first set of example candidate questions generatedby a question generation model trained on known question and answerpairs.

FIG. 18 illustrates a second set of example candidate questionsgenerated by a question generation model trained on known question andanswer pairs.

FIG. 19 illustrates embodiments of a method for training a questiongeneration model.

FIG. 20 illustrates an example provider network environment according tosome embodiments.

FIG. 21 is a block diagram of an example provider network that providesa storage service and a hardware virtualization service to customersaccording to some embodiments.

FIG. 22 is a block diagram illustrating an example computer system thatmay be used in some embodiments.

DETAILED DESCRIPTION

The present disclosure relates to methods, apparatus, systems, andnon-transitory computer-readable storage media for indexing andsearching text-based documents using machine learning. Documents areacquired, text from the documents extracted and indexed, etc. to makethem searchable using term-based or question-based queries. Thesetext-based documents including frequently asked questions (FAQs) are tobe searched according to a user query for one or more top ranked (mostrelevant) documents, one or more top ranked passages (where a passage isa limited number of contiguous lines that have been extracted from agiven document), and/or one or more top ranked FAQs.

Detailed herein are embodiments of an enterprise search service thatenables users to intuitively search unstructured data using naturallanguage. It returns specific and personalized answers to questions,giving end users an experience that comes closer to interacting with ahuman expert.

In keyword-based document analysis approaches, it is hard to determineany sort of context of the content. Embodiments detailed herein allowfor document corpus hosted internally or externally to be accessed andindexed those documents. The indexing helps to provide a context of adocument and provides a semblance of “structure” to an unstructureddocument. In some instances, a set of reserved fields for the indexesgives a more uniform context to labels in a document. As such,embodiments of the enterprise search service described below allow forthe answering of factoid and non-factoid (e.g., how, what, why)questions by extracting relevant information from a document corpus.Such questions (e.g., “What is the latest version of software X”) areusually answerable in a few words. In some embodiments, the enterprisesearch service allows for the answering of short questions that can beanswered in a few lines such as those found in a frequently askedquestions document (e.g., “What is the difference between the IPdefault-gateway, IP default-network, and IP route 0.0.0.0/0 commands?”).In some embodiments, the enterprise search service allows for theanswering of descriptive questions through the identification of anentire relevant document where the answer is the entire document. Forexample, “What is the CLI for Brazil?”

Another deficiency in some search systems is showing the user what isrelevant about the search result. While some search results bold aparticular word or phrase in the result, that is the extent of helping auser identify what is the “correct” answer to the search. Detailedherein are embodiments of further emphasizing the “correct” answer basedon the confidence of the machine learning model(s) that found the“correct” answer. Answers that are not as “correct” are either notemphasized, or emphasized in a different manner.

Many enterprises use log analytics or have use cases like customerservice, searching business reports and FAQs, that could potentiallybenefit from embodiments detailed herein. The embodiments detailedenable these enterprises to build smarter enterprise search applicationsthat cover a wider range of sources securely, and provide strong naturallanguage understanding capabilities, at a fraction of the time andcomplexity needed to implement their own search solution.

FIG. 1 illustrates embodiments of an enterprise search service. Theenterprise search service 102 allows for the querying or searching ofdocuments and/or proper subsets thereof of an enterprise using one ormore machine learning models. Details of various aspects of thisenterprise search service 102 are discussed below. Prior to anysearching, the documents and/or proper subsets thereof have beeningested prior to such querying. In some embodiments, the enterprisesearch service 102 provides the capability to ingest documents from datasources 105 internal to a provider network 100 and data sources 106external to the provider network 100 (e.g., stored at a third-partylocation, stored locally, etc.).

An ingestion service 130 allows for the ingestion of documents into theenterprise search service 102. Documents may be pulled from data sources(e.g., in response to a request) and/or pushed from data sources (e.g.,a synchronization of when a document is added or altered). The ingestionservice 130 may also fetch access control lists (ACLs) associated withthe documents. The ACLs can be used to determine if a search result isallowed to be served.

To get documents from the data sources 105 or 106, the ingestion servicecouples to a connector service 180 which offers a plurality ofconnectors to connect to different data sources and receive data (as apush or a pull) from those sources according to the appropriate protocolfor a particular data source. Note that different data sources may usedifferent transmission protocols, storage protocols, encryptionprotocols, etc.

The data connectors of the connector service 180 are configured using acontrol plane 170. This control plane 170 contains workflows forresource management of the enterprise search service 102. The controlplane 170 may also be used to configure a model building pipeline 160which builds specific models, vocabularies, and embeddings to be hostedin the model hosting service 110 and used in answering a query. Notethat in some embodiments, a model management service 150 may be used torefresh a given model.

The ingestion service 130 also extracts text from documents,pre-processes the extracted text (e.g., tokenize, normalize, and/orremove noise), and calls an indexing service to generate index entriesfor text, and causes the documents (or subset thereof) to be stored. Theindexing service 140 indexes documents that have been acquired by theingestion service 130 into one or more indexes 107. An index is a datastructure of organized data that maps the data to a plurality of fields.Each document or subset of a document (e.g., passage) is identified witha unique identifier. In some embodiments, the index is comprised aplurality of JSON documents.

In some embodiments, the index is an inverted index that lists everyunique word that appears in any document and identifies all of thedocuments each word occurs in. An index can be thought of as anoptimized collection of documents and each document is a collection offields, which are the key-value pairs that contain data. Each indexedfield has a dedicated, optimized data structure. For example, textfields are stored in inverted indices, and numeric and geo fields arestored in BKD trees.

The indexing service 140 may be schema-less, which means that documentscan be indexed without explicitly specifying how to handle each of thedifferent fields that might occur in a document. When dynamic mapping isenabled, the indexing service 140 automatically detects and adds newfields to the index. However, as noted below, a schema of reservedfields may be used to map detected data into datatypes. The reservedfields allow for the distinguishing between full-text string fields andexact value string fields, performing language-specific text analysis,optimizing fields for partial matching, and/or the use datatypes thatare not automatically detected.

Once a set of documents has been indexed, a query against that set ofdocuments may be made via an inference service 120. The inferenceservice 120 handles search queries from end users by performing queryunderstanding (query classification and enrichment), invoking theindexing service 140 to get a relevant set of documents for the query,retrieving the relevant set of documents, and invoking one or moremodels of the model hosting service 110 to deduce a search result forgiven query.

Examples of models utilized by the inference service 120 that are run inthe model hosting service 110 include, but are limited to aquestion/answer (e.g., reading comprehension) which extracts answersfrom passages, a document/passage ranking model which sorts documents inan order of relevance with respect to the query, and a FAQ matchingmodel which attempts to identify a correct the right answer for a givenquestion from a given FAQ document.

A frontend 104 of the enterprise search service 102 couples to one ormore search service components 103 to provide a way for externalcommunications (e.g., from edge device 108, etc.) with the enterprisesearch service 102. For example, through the frontend 104 a user maycommunicate with the ingestion service 130 to configure and start aningestion of one or more documents, provide a query to be served by theinference service 120, etc.

As shown, in some embodiments the enterprise search service 102 is aservice provided by a provider network 100. The provider network 100(or, “cloud” provider network) provides users with the ability toutilize one or more of a variety of types of computing-related resourcessuch as compute resources (e.g., executing virtual machine (VM)instances and/or containers, executing batch jobs, executing codewithout provisioning servers), data/storage resources (e.g., objectstorage, block-level storage, data archival storage, databases anddatabase tables, etc.), network-related resources (e.g., configuringvirtual networks including groups of compute resources, content deliverynetworks (CDNs), Domain Name Service (DNS)), application resources(e.g., databases, application build/deployment services), accesspolicies or roles, identity policies or roles, machine images, routersand other data processing resources, etc. These and other computingresources may be provided as services, such as a hardware virtualizationservice that can execute compute instances, a storage service that canstore data objects, etc. The users (or “customers”) of provider networks100 may utilize one or more user accounts that are associated with acustomer account, though these terms may be used somewhatinterchangeably depending upon the context of use. Users may interactwith a provider network 100 across one or more intermediate networks 101(e.g., the internal via one or more interface(s), such as through use ofapplication programming interface (API) calls, via a console implementedas a website or application, etc. The interface(s) may be part of, orserve as a front-end to, a control plane (e.g., control plane 170) ofthe provider network 100 that includes “backend” services supporting andenabling the services that may be more directly offered to customers.

For example, a cloud provider network (or just “cloud”) typically refersto a large pool of accessible virtualized computing resources (such ascompute, storage, and networking resources, applications, and services).A cloud can provide convenient, on-demand network access to a sharedpool of configurable computing resources that can be programmaticallyprovisioned and released in response to customer commands. Theseresources can be dynamically provisioned and reconfigured to adjust tovariable load. Cloud computing can thus be considered as both theapplications delivered as services over a publicly accessible network(e.g., the Internet, a cellular communication network) and the hardwareand software in cloud provider data centers that provide those services.

A cloud provider network can be formed as a number of regions, where aregion may be a geographical area in which the cloud provider clustersdata centers. Each region can include multiple (e.g., two or more)availability zones (AZs) connected to one another via a privatehigh-speed network, for example a fiber communication connection. An AZmay provide an isolated failure domain including one or more data centerfacilities with separate power, separate networking, and separatecooling from those in another AZ. Preferably, AZs within a region arepositioned far enough away from one other that a same natural disaster(or other failure-inducing event) should not affect or take more thanone AZ offline at the same time. Customers can connect to AZ of thecloud provider network via a publicly accessible network (e.g., theInternet, a cellular communication network).

To provide these and other computing resource services, providernetworks 100 often rely upon virtualization techniques. For example,virtualization technologies may be used to provide users the ability tocontrol or utilize compute instances (e.g., a VM using a guest operatingsystem (O/S) that operates using a hypervisor that may or may notfurther operate on top of an underlying host O/S, a container that mayor may not operate in a VM, an instance that can execute on “bare metal”hardware without an underlying hypervisor), where one or multiplecompute instances can be implemented using a single electronic device.Thus, a user may directly utilize a compute instance (e.g., provided bya hardware virtualization service) hosted by the provider network toperform a variety of computing tasks. Additionally, or alternatively, auser may indirectly utilize a compute instance by submitting code to beexecuted by the provider network (e.g., via an on-demand code executionservice), which in turn utilizes a compute instance to execute thecode—typically without the user having any control of or knowledge ofthe underlying compute instance(s) involved.

Circles with numbers inside of them represent exemplary actions that maybe taken for performing an inference (query). At circle 1, an inferencerequest is sent by an edge device 108 to the enterprise search service102. The frontend 104 calls the inference service 120 which beginsprocessing the request at circle 2.

The processing of the request includes accessing one or more indexes 107via the indexing service 140 at circle 3 to get identifiers of sets ofdocuments to analyze, accessing the identified sets of documents (ortext thereof) from document storage 109, and providing the documents (ortext thereof) and the query to one or more machine learning models inthe model hosting service 110 at circle 5 to determine one or more oftop documents, a top passage, and/or a top FAQ.

The result of the determination by the one or more machine learningmodels is provided the requestor at circle 6 (subject to anyrestrictions). The provision of the result may also include using anenhanced display.

FIG. 2 illustrates embodiments of the enterprise search service 102 usedfor providing inference functionality. In particular, the aspects shownmay be used to respond to a search query on a set of documents. Thefrontend 104 takes in a search request (or query) and provides thatrequest to an inference orchestrator 220 of the inference service 120.

In some embodiments, the query is submitted as an applicationprogramming interface (API) call. In some embodiments, a defaultresponse to such a query includes a relevant passage, a matching FAQ,and a relevant document. The query may contain one or more fieldsindicating how the search is to be performed and/or what is to bereturned. This one or more fields include, for example, one or more of:an attribute filter field which enables filtered searches based ondocument attributes; an exclude document attributes field indicatingwhat attributes to exclude from a response; a facets field defining whatdocument attributes to count; an include document attributes fieldindicating the document attributes to include in a response; an indexidentifier field indicating the index(es) to search; a page number fieldindicating the number of pages of results to return; a page size fieldindicating the size of pages of results to return; a query result typeconfiguration field which sets the type of query (e.g., FAQ, passage,document); a query text field which includes a string of text to searchfor; and a user context field which identifies the end user making thequery so it can be determined if the query result should be filteredbased on the user (e.g., an access control list indicates that the useris not allowed to see the content such a regular employee searching forhealth records of another employee).

The inference orchestrator 220 co-ordinates various services to performan inference using the query. In some embodiments, the inferenceorchestrator 220 includes a state machine or algorithm defining theactions to take. In some embodiments, the inference orchestrator 220performs query classification and enrichment (or couples to a componentthat does). For example, in some embodiments, key phrases, entities,syntax, topics, and/or classifications are extracted. In someembodiments, a classifier machine learning model determines what type ofquestion is being presented. Factoid questions and non-factoid questionsmay get different treatment with respect to what models are used todetermine top results and how results are shown.

The inference orchestrator 220 couples to the indexing service 140 andutilizes the indexing service 140 to access one or more indexes 107 toget matching document identifiers for the query. The indexes 107 includea FAQ index 107A, a question/answer index 107B, and a document/passageindex 107C. In some instances, the inference orchestrator 220 providesan indication of what index(es) to use. In some embodiments, themetadata 210 provides a physical location of the indexes 107 for theindexing service 140 to use.

The result (e.g., document identifiers) of various index queries arereceived by the inference orchestrator 220 to use to retrieve one ormore documents for use by one or more machine learning models (e.g., FAQmodel 212C, question/answer model 212B, and document/passage rankingmodel(s) 212A) hosted by the model hosting service 110. The inferenceorchestrator 220 retrieves the identified documents (e.g., an entiredocument, passage, or FAQ) from text/document storage 109 using documentstorage service 208. The retrieved documents are then supplied, alongwith aspects of the query, to one or more of the models 212A-C of themodel hosting service 110 to identify one or more of: one or more topranked documents, one or more top ranked passages, and/or one or moretop ranked FAQs. Note that the models 212A-C provide confidence scoresof their outputs. Note too that the document storage service 208 storesdocument artifacts that will be used at the time of inference to extractthe answer for a given query.

FIG. 3 illustrates embodiments of the enterprise search service 102 usedfor providing inference functionality. A query 300 is received by theinference orchestrator 220. This is shown at circle 1. The inferenceorchestrator 220 fires the query against one or more indexes 107.

In some embodiments, the query is fired against the document index 107Aand the passage index 107B (shown at circle 2). An identification of setof “top” documents (e.g., top 1,000 documents) and “top” passages (e.g.,5,000 passages) are provided from the indexing service 140 back to theinference orchestrator 220. The associated documents and passages areretrieved (shown at circle 3) and then sent to the document/passageranking model(s) 212A.

The document/passage ranking model(s) 212A analyzes and re-ranks the topdocuments based on relevance scores and, for a top subset (e.g., 100) ofthe ranked documents, determines a set number (e.g., 3) of passages foreach of the top subset of ranked documents (shown at circle 4). In someembodiments, a feature-based deep cross network (DCN) analyzes andre-ranks the top documents. Further, in some embodiments, abidirectional encoder representations from transformers (BERT) modeltakes the ranking top subset of documents and finds the passages andoutputs relevance scores. The relevance scores of the DCN and BERT arecombined to obtain the final reranking of top documents. In someembodiments, when the data is purely textual documents with no metadatafields, then the DCN can be bypassed, and only the BERT used forreranking of the top 100 documents directly. Note that an output of thedocument/passage ranking model(s) 212A is a set of top rankingdocument(s) 304 and/or top ranking passages. In some embodiments, thetop ranking passages are found using a union of the top rankingdocuments and the indexed passages.

The question and answer model 212B is used to determine a set of one ormore top passages for the query. The query is fired against the passageindex 107B at circle 5 to find a top number (e.g., 100) of passageswhich are retrieved and sent to the document/passage ranking model(s)212A for analysis and reranking. In particular, in some embodiments, theBERT model receives the top passages and re-ranks the passages and sendstop few (e.g., 5) to the question and answer model 212B at circle 6. Insome embodiments, the question and answer model 212B is also BERT-based.The question and answer model 212B analyzes these few passages andoutputs a top passage 306 that is, at times, highlighted) with multipleanswer candidates. In some embodiments, when the top passage'sconfidence score exceeds a first threshold it is displaced. In someembodiments, when aspects the top passage's confidence score exceeds asecond, more stringent threshold, those aspects of the top passage arehighlighted as the best answer while less confident scores are otherwiseenhanced (e.g., bolded).

The FAQ model 212C is used to determine a set of one or more top FAQsfor the query. The query is fired against the FAQ questions index 107Cat circle 7 and the top set of matching questions are sent to the FAQmodel 212C from the text/document storage 109. The FAQ model 212Cre-ranks the top set of questions and returns the most relevantquestions along with their answers 308. In some embodiments, the FAQmodel 212C is a BERT-based model.

FIG. 4 illustrates embodiments of a method for performing an inference(search on documents). Some or all of the operations (or other processesdescribed herein, or variations, and/or combinations thereof) areperformed under the control of one or more computer systems configuredwith executable instructions and are implemented as code (e.g.,executable instructions, one or more computer programs, or one or moreapplications) executing collectively on one or more processors, byhardware or combinations thereof. The code is stored on acomputer-readable storage medium, for example, in the form of a computerprogram comprising instructions executable by one or more processors.The computer-readable storage medium is non-transitory. In someembodiments, one or more (or all) of the operations are performed bycomponents of the other figures such as under the control of theinference orchestrator 220 which calls the indexing service 140 fordocument identifiers, fetches the identified documents using thedocument storage service 208, and calls one or more ML models in themodel hosting service 110 to analyze the fetched documents.

At 401, a search query is received in a frontend. The search queryincludes a question to be answered. In some embodiments, the searchquery includes an indication of what type of answer is expected (e.g., alisting of documents, a passage, and/or an FAQ). For example, prefix (orpostfix) such as passage:QUESTIONTEXT may be used. Or, a selection froma list of potential results may be used. Examples of a search query APIcall have been detailed above.

The search query is performed to generate one or more results at 402.Documents that matches the search query are identified by querying oneor more indexes at 403. For example, in some embodiments, an index fordocuments is queried for a set “top” matching documents at 405, an indexfor passages is queried for a set of “top” matching passages at 406,and/or an index for FAQs is queried for a set of “top” FAQs at 407. Notethat these indexes may be independent of each other or combined in anymanner. As noted, an inference orchestrator may cause these one or morequeries to occur. In some embodiments, the query is formed such that isrequests a “match” for the words of the question. A match query returnsdocuments that match provided text, numbers, dates, or Boolean values.The match query may limit the number of results, the number of words ofthe question to use, etc.

At 409, the identified documents, passages, and/or FAQs are fetchedaccording to the matched data at 409. As discussed, an inferenceorchestrator may cause these one or more fetches to occur. Thedocuments, passages, and/or FAQs may be stored in separate locations ortogether. Additionally, the documents, passages, and/or FAQs may bepre-processed to make subsequent analysis easier. In some embodiments,the fetching is of whole documents. In some embodiments, the fetching isof extracted text from the documents.

One or more of a top ranked passage, top ranked documents, and a topranked FAQ are determined from the fetched documents, passages, and/orFAQs based upon one or more invocations of one or more machine learningmodels for the search query at 411. Several operations may occur in thisaction. Note the models produce a confidence score for their results.

In some embodiments, a proper subset of the identified (fetched) set ofdocuments are determined using a first machine learning model at 413.For example, in some embodiments, the fetched documents are rerankedusing a first model (e.g., DCN model) according to relevance scores andthen a second model (e.g., BERT-based) looks at some top number of thosereranked documents (e.g., 100) and uses top passages from the retrievedpassages for those top documents to determine a relevance score perdocument. The relevance scores from the first and second models arecombined to generate a set of top ranked documents. In otherembodiments, only the reranking using the first model is performed.

In some embodiments, a proper subset of the identified (and fetched) setof passages is identified using a second machine learning model basedupon the query and fetched passages at 417. This proper subset is areranking of the passages. This may be the same model as the BERT-basedmodel detailed as being used at 413. This reranked subset is provided toa third model (along with aspects of the query) which determines a toppassage from the reranked subset at 419. The third model is a BERT-basedmodel in some embodiments.

In some embodiments, a proper subset of the identified (and fetched) setof FAQS is determined using a fourth machine learning model on thefetched FAQs and the query at 421. This proper subset includes the topranked FAQ. In some embodiments, the fourth machine learning model isBERT-based.

One or more of the top ranked passage, the top ranked documents, and thetop ranked FAQ are returned at 423. The return may include displayingthe result. In some embodiments, what is returned is subject to anaccess control list and/or confidence score threshold. For example, ifthe top ranked document, etc. is not allowed to be shared with thesearching user based on the access control list, then either nothing isreturned, or a lower ranked document, etc. is returned instead. In someembodiments, an improved display of the result is utilized. Note that insome embodiments, the returned result is sent back through the frontendand/or inference orchestrator.

At 425, feedback is received on the one or more of the returned one ormore of the top ranked passage, the top ranked documents, and the topranked FAQ in some embodiments. This feedback may be used to tune themodels.

FIG. 5 illustrates embodiments of an improved display of a result of aninference query. As shown, a graphical user interface (GUI) 500 allowsfor a user to input a query using a query input mechanism 504 (e.g., aninput box). In some embodiments, the user may further define the datasetusing a dataset indicator/selector 502. For example, the user may definethat HR documents are to be queried, or that HR FAQs are to be queried,etc. In some embodiments, by default documents, passages, and FAQs areall queried.

The GUI 500 provides an indication of a number of results 506 that arereturned along with the results 508, 518 themselves. In someembodiments, an answer 505 to the question being asked is extracted andshown proximately. Note that there may be a result shown per index type(e.g., document, passage, and FAQ). In this example, the first result508 shows text 510 which includes highlighting for a particularlyrelevant aspect of the result. In particular, the text “RESULT” has beenhighlighted from a document's text. This highlighted text is the topranked text (or at least the top ranked text that the user is allowed tosee) where the result exceeds one or more confidence score thresholds.In some embodiments, the highlighting is shown using a font change andin some embodiments the text is color highlighted (as in using a yellowbackground for the section of text). The first result 508 also includesthe location of the result 512 (e.g., the document location) and a meansto provide feedback 514, e.g., as feedback input 1106 in FIG. 11.

The second result 518 shows text 520 that less emphasizes a relevantaspect of the result. In particular, the text “RESULT” has beenemphasized from a document's text, but in a less conspicuous way thanthe highlighted text. This emphasized text is the top ranked text (or atleast the top ranked text that the user is allowed to see) where theresult exceeds one or more confidence score thresholds (but not as muchas a highlighted text would). The emphasis may be bolding, italicizing,underlining, changing the font size, etc. The second result 518 alsoincludes the location of the result 522 (e.g., the document location)and a means to provide feedback 524, e.g., as feedback input 1106 inFIG. 11.

Feedback input may be in the form of an API request that includes one ormore parameters such as one or more of: click feedback items (alert thatthe search result was taken), an identifier of the index that wasqueried, an identifier of the query itself, relevance such as athumbs-up or thumbs-down.

FIG. 6 illustrates embodiments of a method for performing an improveddisplay of a result of an inference query. Some or all of the operations(or other processes described herein, or variations, and/or combinationsthereof) are performed under the control of one or more computer systemsconfigured with executable instructions and are implemented as code(e.g., executable instructions, one or more computer programs, or one ormore applications) executing collectively on one or more processors, byhardware or combinations thereof. The code is stored on acomputer-readable storage medium, for example, in the form of a computerprogram comprising instructions executable by one or more processors.The computer-readable storage medium is non-transitory. In someembodiments, one or more (or all) of the operations are performed bycomponents of the other figures.

A search query is received at 601. For example, a search query isreceived at the frontend 104 and passed to the inference orchestrator220. Examples of search queries have been detailed above.

The search query is performed to generate one or more results at 603.Embodiments of such performance have been detailed above (e.g., withrespect to at least FIG. 4). The results may include, but are notlimited to: text from top ranking documents, a top ranked passage, a topranked FAQ, etc. In some embodiments, the performance of the searchquery includes using one or more ML models.

The one or more results are displayed at 605. Note that in someembodiments, the returned result is sent back through the frontend 104and/or inference orchestrator 220 for display. It is typically in one ofthese components that a determination of what can be shown and/oremphasizing certain aspects of a result is/are made. For example, theapplication of an access control list, etc.

In this example, it is assumed that the result is allowed to bedisplayed, but how the result is to be displayed differs depending uponhow confident the underlying models were in their analysis. At 607, adetermination is made as to if an aspect of the result exceeds a firstconfidence threshold. For example, does the result from one or more MLmodels indicate that the result is deemed to be fairly correct based onthe confidence score of the one or more ML models. When the confidencescore is low, this indicates that the result may not be particularlygood. When the first threshold is not met, then the result is either notshown, or there is no emphasis in the display of the result at 609.

When the first threshold is met, then the result is shown with emphasis.The type of emphasis may differ depending upon a determination whetheran aspect of the result exceeds a second confidence threshold that isgreater than the first threshold made at 611. When the second thresholdis not met, then a first type of emphasis is used to emphasize theaspect at 613. Examples of a first type of emphasis include, but are notlimited to: bolding, underlining, changing a font, changing a font size,and italicizing. When the second threshold is met, then a second type ofemphasis is used to emphasize the aspect at 615. This second type ofemphasis is meant to stand out more than the first type of emphasis andmay include highlighting, bolding, underlining, changing a font,changing a font size, italicizing, or a combination thereof. Note thatthe first and second type of emphasis are different.

FIG. 7 illustrates embodiments of the enterprise search service 102 usedfor providing ingestion functionality. The frontend 104 takes in intakerequests, index creation requests, etc. and passes those requests to theingestion service 130. The ingestion service 130 performs documentvalidation on documents retrieved from data sources 105/106. In someembodiments, the ingestion service 130 co-ordinates various services toperform index creation, index updating, and other ingestion tasksdetailed below. In other embodiments, the ingestion service placesdocuments to be processed in a queue 735 which includes an extractionand pre-preprocess pipeline. intake requests ask that a set of documentsbe in taken such that the documents are acquired, indexed,pre-processed, stored, etc.

As shown, the ingestion service 130 is coupled to a plurality ofservices. The connector service 180 receives (either as a push or apull) documents from data sources 105/106 where the physical locationmay be provided by metadata 210. The indexing service 124 is pullsdocuments and/or text from the queue 735 (which may be pre-processed)and to create or update indexes 107 associated with documents (includingpassages and FAQs). The metadata 210 may provide the physical locationof those indexes 107. The document storage service 208 also pullsdocuments from the queue 735 to store documents to store documents andchunks thereof in text/document storage 109.

Prior to updating an index, it needs to be created. In some embodiments,a create index API call is received by the frontend 104 which calls theindexing service 124 to generate an index of indexes 107. The createindex request includes one or more fields to inform the behavior of theindexing service 124 such as a field for a description of the index,field for an index name, a field for the role that gives permission forlogs and metrics, a field identifying an encryption key, etc.

When an index has been created, it can be updated. This updating may bein the form of a single update or batch update which causes an ingestionof text and unstructured text into an index, the addition of customattributes to the documents (should it be desired), an attachment of anaccess control list to the documents added to the index, a storage ofthe text, a pre-processing of the text (and storage thereof), etc. Insome embodiments, a update request includes one or more fields to informthe behavior of the indexing service 124, the document storage service208, the queue 735 include extraction and pre-processing pipeline, andthe connector service 180 and includes one or more fields such as afield for a location of one or more documents, a field for the documentsthemselves, a field for the index's name, a field for the role thatgives permission for logs and metrics, etc. The extraction andpre-processing pipeline extracts text from documents and pre-processesthem (e.g., tokenize, etc.). In some embodiments, the extracted text(e.g., tokens) is broken down into overlapping passages using slidingwindows by the extraction and pre-process pipeline. The overlappingpassages are then indexed and/or stored.

FIG. 8 illustrates embodiments of a method for performing ingestion ofone or more documents. Some or all of the operations (or other processesdescribed herein, or variations, and/or combinations thereof) areperformed under the control of one or more computer systems configuredwith executable instructions and are implemented as code (e.g.,executable instructions, one or more computer programs, or one or moreapplications) executing collectively on one or more processors, byhardware or combinations thereof. The code is stored on acomputer-readable storage medium, for example, in the form of a computerprogram comprising instructions executable by one or more processors.The computer-readable storage medium is non-transitory. In someembodiments, one or more (or all) of the operations are performed bycomponents of the other figures.

At 801, an intake request is received. For example, the frontend 104receives the intake request.

One or more documents are acquired from one or more data sourcesaccording to the request at 803. For example, when the request indicatesa particular storage bucket, documents are gathered from that bucket.Note that acquiring could simply be pulling one or more documents fromthe intake request.

In some embodiments, the documents are acquired by crawling of one ormore data sources at 805. This crawling of documents is performed by theconnector service 180 in some embodiments and may include gathering frominternal and/or external sources.

In some embodiments, the ACL for the acquired one or more documents isfetched at 807. As noted above, ACLs may be used to determine whatresults a user can see for a performed inference. The ACL may be storedwith the document or pointed to by the document's owner.

Text is extracted from the acquired one or more documents andpre-processed at 809. Metadata may also be extracted. For example, textmay be extracted from a document that includes non-text such as images.The pre-processing of the extracted text includes one or more oftokenizing, normalizing, and/or removing noise. This is performed by theextraction and pre-processing pipeline 735 per document acquired. Notethat extracted text may include passages.

The extracted text and pre-processed text are stored at 811. This may beperformed by the document storage service 208 which puts this extractedtext and pre-processed text in text/document storage 109. The textand/or pre-processed text are used during inference.

At 813, index entries for the extracted text are generated. In someembodiments, the index entry includes a pointer to an ACL. Thegeneration of the index includes mapping labels of the document intofields for the index entry. In some embodiments, this mapping utilizesreserved fields. Reserved fields are “default” fields that allow forstandardization across multiple different accounts. This standardizationmay help in the training of the models used in an inference asdeveloping a corpus of training data should be easier when common labelsare used (as opposed to training using different labels per useraccount). For example, a reserved field of “TITLE” allows user account 1and user account 2 to use the same label in their documents. In someembodiments, existing labels are mapped to the reserved fields. Thatmapping may be automatic or according to a mapping provided by anadministrator.

In some embodiments, the underlying acquired documents are stored at815.

FIG. 9 illustrates embodiments of exemplary reserved fields for use iningestion. Documents can use labels to indicate what the text is. Forexample, a label indicating the text is the title. As discussed above,index entries include fields for the text content and these fieldscorrespond to labels. In the enterprise search service 102 describedherein a set of “reserved” labels may be utilized in index entry fields.These reserved labels allow for text to be labeled in a common mannerbetween documents, users, etc.

Examples of names of “reserved” fields 901 and their correspondingdatatypes 903 are shown. In some embodiments, body and title aredefault. While these fields are “reserved,” in some embodiments thesefields are updatable. For example, if the name “modification date” isnot what anyone is using, it can be changed to reflect usage.Additionally, new “reserved” fields may be added as needed or desired.Note that the use of “reserved” fields may be overridden in someembodiments.

FIG. 10 illustrates embodiments of a graphical user interface to be usedin updating/adding/removing reserved fields for use in ingestion. Asshown, a GUI 1000 allows for a user to adjust reserved fields includingadding a field, removing a field, and updating a field.

The GUI 1000 includes a reserved field search mechanism 1004 (e.g., aninput box). In some embodiments, the user may further define the datasetusing a dataset indicator/selector 1002. For example, the user maydefine that HR documents have a certain set of reserved fields, whereasfinance documents use a different set.

The display includes, per reserved field, a reserved field name 1006, anexplicit mapping to the name field 1008, data type field 1012, and anindication of if the reserved field is used. The reserved field name1006 is the label used by the indexing service 140. The explicit mappingto the name field 1008 allows for a user to provide to the indexingservice 140 a mapping of labels in existing documents to the reservedfield. The fields 1006, 1008, and 1012 are editable and the applicationof the update field functionality 1016 commits changes.

When the add field functionality 1018 is used, a new reserved fieldentry is added allowing a user to add reserved field name, an explicitmapping to the name field, data type field, and an indication of if thereserved field is used. This may be performed using one or more GUIs(not shown).

A field can be removed using the use field 1014 and then applying theremove field functionality 1014.

FIG. 11 illustrates embodiments of a model building system. Modelbuilding system may be utilized to build the model and refresh it.Depicted model building system includes frontend 104 coupled to aningestion service 130, a model building pipeline 160 coupled to theingestion service 130, a metric aggregator 1102, a control plane 170,and a model storage service 1104. Depicted model building systemincludes metric aggregator 1102 coupled to control plane 170, and modelstorage service 1104 coupled to model management service 150. Depictedmodel management service is coupled to model hosting service 110, whichmay host one or any combination of: document/passage ranking model(s)212A, question/answer model 212B, and FAQ model 212C.

In one embodiment, ingestion service 130 receives a document ordocuments that are to be ingested, and sends a report of the ingestionmetrics (e.g., metrics that indicate the number of documents, the indexsize of the corpus of documents, index failures, etc.) to metricaggregator 1102, metric aggregator 1102 polls if document corpus haschanged enough (e.g., exceed a threshold) to trigger model building,when the model build is triggered, an indication is sent to controlplane 170 such that the control plane cause the model building pipeline160 to build the model (e.g., machine learning model), the built modelis then saved by model storage service 1104. Model building system mayfurther include a training data generation service 1108, e.g., to createtraining data 1110 from the user's data. Training data 1110 may beutilized by the model building pipeline 160 in creation of a modeland/or used by model management service 150 in the refresh of a model.

A model (e.g., at or after initial use) may have improved functionalitywith further training. The training may be based at least in part onfeedback input 1106, e.g., feedback provided by a user. In certainembodiments, a model management service 150 is to pull the model (e.g.,from model storage service 1104) and refresh it (e.g., based on feedbackinput 1106). Refreshing the model may include utilizing the feedback(e.g., from feedback input 1106 or other feedback) in a next trainingiteration of the model. A next version of the model formed from the nexttraining iteration may then be used by saving the model to model hostingservice 110, e.g., where the updated model is one or any combination of:document/passage ranking model(s) 212A, question/answer model 212B, andFAQ model 212C. A model refresh (or displaying of a proper subset of thedata for labeling by the user in active learning) may be triggered whena confidence value (e.g., score) of a proper subset of the data (e.g.,answers and/or documents) the model returns for a search query fallsbelow a confidence threshold. Additionally or alternatively, a modelrefresh (or displaying of a proper subset of the data for labeling bythe user in active learning) may be triggered in response to exceeding aconfidence difference threshold for a difference between a firstconfidence score for a first section (e.g., a first, highest scoredcandidate answer or candidate document) of the proper subset of the datawith respect to its relevance to the search query and a secondconfidence score for a second section (e.g., a second, next highestscored candidate answer or candidate document) of the proper subset ofthe data with respect to its relevance to the search query. A propersubset of the data (e.g., answers and/or documents) for presentation tothe user (e.g., to be used for labeling by the user in active learning)may be selected based on a confidence value of the proper subset of thedata the model returns for a search query.

The feedback input 1106 may include click-through data (e.g., how manytimes a provided link is selected by a user) and/or customer annotateddata (e.g., as discussed below in reference to FIGS. 13 and 14).

A model may include an input of a search query for a search of ingesteddata (e.g., the user's documents) and an output of a best answer from aplurality of answers from the data and/or an output of a best documentfrom a plurality of documents from the data. Active learning may beutilized to train the model where a user is to request that the userindicate the desired output (e.g., answer(s) or document(s)) for aninput (e.g., search query). However, instead of requiring a user toindicate which answers and/or documents are the most important for asearch query (e.g., question) from the entirety (or substantially theentirety) of the data, certain embodiments herein present a propersubset of the data to the user for their indication which answers and/ordocuments are the most important for a search query. Thus, theseembodiments allow a user to perform the labeling (e.g., as beingimportant enough to use for a next iteration of training) of the datawithout overwhelming them with uninformative document(s).

In one embodiment, based on the current performance of the model, activelearning is applied to suggest a specific subset of user query (orqueries) and document candidates and/or answer candidates to a user(e.g., a human) for labeling. Thus, the suggested subset of documentcandidates and/or answer candidates provide more value towards improvingthe machine learning model compared to a randomly sampled set. There maybe different approaches for active learning. One example is to check thedifference between the confidence scores from the top candidates, e.g.,and if the difference is less than a threshold, the model then needsrefinement on such queries. Another example is diversity sampling inorder to have larger coverage of the data. Next, a user (e.g., customer)can label the relevance of the document and answer candidates (e.g., byselecting an interface element of a graphical user interface). Incertain embodiments, when a threshold amount of annotated data (e.g.1000 samples) are received, the machine learning model is retrainedusing the annotated data (e.g., and the accuracy of the retrained modelis evaluated on a held-out set of data from the user's data (e.g.,ingested data)). In one embodiment, if the improvement exceeds a certainthreshold, the previous version of the model is replaced with theretrained model. The above can be repeated based on a pre-determinedschedule.

FIG. 12 illustrates embodiments of a method for model management, forexample, implemented by model management service 150. Depicted modelmanagement includes (optionally) receiving a search query 1201,(optionally) performing a search of data of a user, using a machinelearning model, for the search query to generate a result 1203, and(optionally) providing a result of the search to the user 1205. Activelearning 1207 includes generating a confidence score (e.g., by themachine learning model) based on the result of the search 1209,selecting a proper subset of the data based at least in part on aconfidence score of the proper subset of the data 1211, displaying theproper subset of the data to the user 1213, receiving an indication fromthe user of one or more sections of the proper subset of the data foruse in a next training iteration of the machine learning model for thesearch query 1215, performing the next training iteration of the machinelearning model with the one or more sections of the proper subset of thedata 1217, and (optionally) replacing a previous version of the machinelearning model used for the search query with a next version generatedfrom the next training iteration when an accuracy score for the nextversion exceeds an accuracy score for the previous version 1219. Afterperforming active learning 1207, (optionally) another search query maybe received 1221, then performing the another search query of data ofthe user using the machine learning model trained with the one or moresections of the proper subset of the data 1223, and providing a resultof the another search to the user 1225.

FIG. 13 illustrates embodiments of a graphical user interface 1300 to beused in active learning of question and answer(s) for training a machinelearning model. Depicted graphical user interface 1300 includes a field1302 that is customizable with text to indicate that the user is to takean action (e.g., “Please select the following answer(s) that arerelevant to the indicated query”) and a field 1304 to be populated withthe query that the user is to label the answers for relevancy.Optionally, the number of candidate answers 1306 may be indicated.

Graphical user interface 1300 includes a plurality of entries 1308A-Band each entry includes a feedback input 1310A-B, respectively. Althoughtwo entries are shown, any plurality of entries may be utilized (e.g.,where “X” is any positive integer). In the depicted embodiment, a query1304 that is to have active learning performed on it is provided as wellas a plurality of candidate answers, e.g., as discussed herein. Forexample, with candidate answers including a passage 1312 with the answerhighlighted (e.g., bold, underlined, marked as a different color, etc.)and the passage of surrounding text also included (e.g., to providecontext to the user for their reading comprehension of the answer andits possible relevancy to the query). A link 1314 may also be includedto the source document.

As depicted, the example query 1304 is “how much does package pick-upcost?”. Candidate answer 1 1308A includes a passage 1312A stating “Thereis no charge for this option. We will pick up your return at the addressof your choice.” A user may deem that candidate answer 1 to be relevant(e.g., a most important answer(s)) and mark feedback input 1310A (shownas a checkbox as an example). Candidate answer 2 1308B includes apassage 1312B stating “If you choose the Pickup option and the return isnot a result of our error, you will be charged a convenience fee of$XX.XX for the Pickup.” (where XX.XX is an actual number value). A usermay deem that candidate answer 2 to be relevant (e.g., independent ofcandidate answer 1 being relevant) and mark feedback input 1310B (shownas a checkbox as an example). The highlighting may be added to theresult provided by the model, and the surrounding words (e.g., thesentence before and/or after the result) also provided. Feedback input1310 may be another interface element, such as, but not limited to athumbs up (or down), checkbox, button, dropdown menu, etc.

A user may click the submit interface element 1316 to cause the feedbackinput(s) to be sent, e.g., as feedback input 1106 in FIG. 11. Feedbackinput may be aggregated, e.g., to trigger a retrain of a model asdiscussed herein.

FIG. 14 illustrates embodiments of a graphical user interface to be usedin active learning of document ranking for training a machine learningmodel. Depicted graphical user interface 1400 includes a field 1402 thatis customizable with text to indicate that the user is to take an action(e.g., “Please select the following document(s) that are relevant to theindicated query”) and a field 1404 to be populated with the query thatthe user is to label the documents for relevancy. Optionally, the numberof candidate documents 1406 may be indicated.

Graphical user interface 1400 includes a plurality of entries 1408A-Band each entry includes a feedback input 1410A-B, respectively. Althoughtwo entries are shown, any plurality of entries may be utilized (e.g.,where “X” is any positive integer). In the depicted embodiment, a query1404 that is to have active learning performed on it is provided as wellas a plurality of candidate documents, e.g., as discussed herein. Forexample, with candidate documents including a link 1412 to the (e.g.,hosted in storage 109) document.

As depicted, the example query 1404 is “operating manual for widget Y?”.Candidate document 1 1408A includes a link 1412A to a first document. Auser may deem that candidate document 1 to be relevant (e.g., a mostimportant document(s)) and mark feedback input 1410A (shown as acheckbox as an example). Candidate document 2 1408B includes a link1412B to a second document. A user may deem that candidate document 2 tobe relevant (e.g., independent of candidate document 1 being relevant)and mark feedback input 1410B (shown as a checkbox as an example).Feedback input 1410 may be another interface element, such as, but notlimited to a thumbs up (or down), checkbox, button, dropdown menu, etc.

A user may click the submit interface element 1416 to cause the feedbackinput(s) to be sent, e.g., as feedback input 1106 in FIG. 11. Feedbackinput may be aggregated, e.g., to trigger a retrain of a model asdiscussed herein.

FIG. 15 illustrates embodiments of a method for active learning fortraining a machine learning model. Depicted method includes performing asearch of data of a user, using a machine learning model, for a searchquery to generate a result 1501, generating a confidence score for theresult of the search 1503, selecting a proper subset of the data to beprovided to the user based on the confidence score 1505, displaying theproper subset of the data to the user 1507, receiving an indication fromthe user of one or more sections of the proper subset of the data foruse in a next training iteration of the machine learning model 1509, andperforming the next training iteration of the machine learning modelwith the one or more sections of the proper subset of the data 1511.

In certain embodiment, the models discussed herein (e.g.,document/passage ranking model(s) 212A, question/answer model 212B, andFAQ model 212C) are trained with a set of training data. Training datamay include a question and a corresponding answer from a user's data(e.g., in contrast to public data or data from other enterprises).However, the generation of such pairs of questions and answers mayrequire annotation by a human, and thus be costly in terms of time andexpense and/or prone to human errors. Training data may be used by amodel building system (e.g., model building system depicted in FIG. 11).Model building system may include a training data generation service(e.g., training data generation service 1108 in FIG. 11), for example,to create training data 1110 from a user's data. Training data (e.g.,training data 1110 in FIG. 11) may be utilized by a model buildingpipeline (e.g., model building pipeline 160 in FIGS. 1 and 11) increation of a model and/or used by a model management service (e.g.,model management service 150 in FIGS. 1 and 11) in a refresh of a model.

Certain embodiments herein remove a human from generating training data,for example, by removing the human from identifying a question for ananswer and/or identifying an answer from a question. These embodimentsmay include training a language machine learning model to identify(e.g., generate) a set of question and answers pairs from a user's data(e.g., from their unstructured text data) without requiring humanannotation or other human involvement.

In one embodiment, a request to build a model (e.g., with model buildingpipeline 160 in FIGS. 1 and 11) causes a service (e.g., training datageneration service 1108 in FIG. 11) to generate training data from auser's data. Training data may include questions and their correspondinganswers, e.g., candidate questions generated by the service for probableanswers in a user's data. Completed training data may then be providedto a model building pipeline for use in building a model specificallyfor a user's data.

FIG. 16 illustrates embodiments of a method for training and use of aquestion generation model. In certain embodiments, the training data tobe generated is a set of one or more candidate questions from a user'sdata (e.g., the user's documents, passages, etc.) containing answers.Depicted method in FIG. 16 includes training of a question generationmodel 1601. In one embodiment, the training of question generation model1601 includes training a (e.g., language) machine learning model withknown question and answer pairs to predict a question from an answer1603. The known question and answer pairs may be data that does notinclude the user's data (e.g., data not from the user), e.g., this datamay be public data. Examples of “known question and answer pairs” arewithin a MAchine Reading COmprehension (MARCO) dataset. The knownquestion and answer pairs may be public data, e.g., in contrast to theuser's private data (e.g., that is hosted in storage 109 in FIG. 1).

One example of a language machine learning (ML) model is atransformer-based language model that predicts the next word of a stringof text based on the previous words within the string of text. Certainembodiments herein modify a language ML model used to predict the nextword for each successive word of a string of text to predict each next(e.g., successive) word of a question for a given answer, e.g., topredict each successive word of a known question (e.g., a multiple wordquestion) from its known answer (e.g., a multiple word answer). Oneexample language ML model is a transformer model (e.g., a GPT-2 model)that is first trained on (e.g., a very large amount of) data in anunsupervised manner using language modeling as a training signal, and issecond fine-tuned on much smaller supervised datasets (e.g., knownquestions and their corresponding known answers) to help it solvespecific tasks.

Referring to FIG. 16, training 1601 includes training a (e.g., language)machine learning model with known question and answer pairs to predict aquestion from an answer 1603, receiving one or more documents from auser 1605, generating a set of question and answer pairs from the one ormore documents from the user using the trained machine learning model1607, and (optionally) storing the set of question and answer pairs(e.g., in training data 1110 storage in FIG. 11) generated from the oneor more documents from the user 1609.

In certain embodiments, the training data (e.g., which has beengenerated by a machine and not by a human or using human's annotations)is then used to train another machine learning model. For example,training a second machine learning model (e.g., document/passage rankingmodel(s) 212A, question/answer model 212B, and FAQ model 212C), with theset of question and answer pairs (e.g., from training data 1110 storagein FIG. 11) generated from the one or more documents of the user's, todetermine one or more top ranked answers from data of the user for asearch query from the user 1611.

After training the second machine learning model, it may then be used,for example, after receiving a search query from the user 1613, forperforming the search query on data (e.g., documents) of the user usingthe second machine learning model 1615, and providing a result of thesearch query to the user 1617, e.g., a result of one or more of a topranked document, top ranked passage, or top ranked question.

In certain embodiments, the language ML model is also trained to detectan end of question (EOQ) token. In certain embodiments, the input to alanguage model to generate training data (e.g., generate questions forknown answers) includes a passage having the answer and the question,for example, in the format of a beginning of service indicator (e.g.,<bos>) followed by beginning of question indicator (e.g., <boq>),followed by the question, and then followed by an end of questionindicator (e.g., <eoq>).

FIG. 17 illustrates a first set of example candidate questions generatedby a question generation model trained on known question and answerpairs. In FIG. 17, example synthetic question generation 1700 includes aknown question 1702 (e.g., with an end of question token), a passagewith a known answer 1704 to the known question 1702, and illustrates thecandidate questions (e.g., and their end of question token <eoq>)generated by the model 1706 trained to generate synthetic questions fromthose two inputs 1702 and 1704. The model trained to do so may thus beused on a user's data to generate specific training data based on theuser's (e.g., customer's) documents, e.g., instead of only being trainedon other's documents.

FIG. 18 illustrates a second set of example candidate questionsgenerated by a question generation model trained on known question andanswer pairs. In FIG. 18, example synthetic question generation 1800includes a known question 1802 (e.g., with an end of question token), apassage with a known answer 1804 to the known question 1802, andillustrates the candidate questions (e.g., and their end of questiontoken <eoq>) generated by the model 1806 trained to generate syntheticquestions from those two inputs 1802 and 1804. The model trained to doso may thus be used on a user's data to generate specific training databased on the user's (e.g., customer's) documents, e.g., instead of onlybeing trained on other's documents. In one embodiment, a same model isused to generate questions 1806 in FIG. 18 and questions 1706 in FIG.17. Trained model may then be used to generate training data that issubsequently used to train a second machine learning model (e.g.,document/passage ranking model(s) 212A, question/answer model 212B, andFAQ model 212C), e.g., and the trained, second machine learning modelused as discussed herein.

FIG. 19 illustrates embodiments of a method for training a questiongeneration model. Depicted method includes receiving one or moredocuments from a user 1901, generating a set of question and answerpairs from the one or more documents from the user using a machinelearning model trained to predict a question from an answer 1903, andstoring the set of question and answer pairs generated from the one ormore documents from the user 1905.

FIG. 20 illustrates an example provider network (or “service providersystem”) environment according to some embodiments. A provider network2000 may provide resource virtualization to customers via one or morevirtualization services 2010 that allow customers to purchase, rent, orotherwise obtain instances 2012 of virtualized resources, including butnot limited to computation and storage resources, implemented on deviceswithin the provider network or networks in one or more data centers.Local Internet Protocol (IP) addresses 2016 may be associated with theresource instances 2012; the local IP addresses are the internal networkaddresses of the resource instances 2012 on the provider network 2000.In some embodiments, the provider network 2000 may also provide publicIP addresses 2014 and/or public IP address ranges (e.g., InternetProtocol version 4 (IPv4) or Internet Protocol version 6 (IPv6)addresses) that customers may obtain from the provider 2000.

Conventionally, the provider network 2000, via the virtualizationservices 2010, may allow a customer of the service provider (e.g., acustomer that operates one or more client networks 2050A-2050C includingone or more customer device(s) 2052) to dynamically associate at leastsome public IP addresses 2014 assigned or allocated to the customer withparticular resource instances 2012 assigned to the customer. Theprovider network 2000 may also allow the customer to remap a public IPaddress 2014, previously mapped to one virtualized computing resourceinstance 2012 allocated to the customer, to another virtualizedcomputing resource instance 2012 that is also allocated to the customer.Using the virtualized computing resource instances 2012 and public IPaddresses 2014 provided by the service provider, a customer of theservice provider such as the operator of customer network(s) 2050A-2050Cmay, for example, implement customer-specific applications and presentthe customer's applications on an intermediate network 2040, such as theInternet. Other network entities 2020 on the intermediate network 2040may then generate traffic to a destination public IP address 2014published by the customer network(s) 2050A-2050C; the traffic is routedto the service provider data center, and at the data center is routed,via a network substrate, to the local IP address 2016 of the virtualizedcomputing resource instance 2012 currently mapped to the destinationpublic IP address 2014. Similarly, response traffic from the virtualizedcomputing resource instance 2012 may be routed via the network substrateback onto the intermediate network 2040 to the source entity 2020.

Local IP addresses, as used herein, refer to the internal or “private”network addresses, for example, of resource instances in a providernetwork. Local IP addresses can be within address blocks reserved byInternet Engineering Task Force (IETF) Request for Comments (RFC) 1918and/or of an address format specified by IETF RFC 4193 and may bemutable within the provider network. Network traffic originating outsidethe provider network is not directly routed to local IP addresses;instead, the traffic uses public IP addresses that are mapped to thelocal IP addresses of the resource instances. The provider network mayinclude networking devices or appliances that provide network addresstranslation (NAT) or similar functionality to perform the mapping frompublic IP addresses to local IP addresses and vice versa.

Public IP addresses are Internet mutable network addresses that areassigned to resource instances, either by the service provider or by thecustomer. Traffic routed to a public IP address is translated, forexample via 1:1 NAT, and forwarded to the respective local IP address ofa resource instance.

Some public IP addresses may be assigned by the provider networkinfrastructure to particular resource instances; these public IPaddresses may be referred to as standard public IP addresses, or simplystandard IP addresses. In some embodiments, the mapping of a standard IPaddress to a local IP address of a resource instance is the defaultlaunch configuration for all resource instance types.

At least some public IP addresses may be allocated to or obtained bycustomers of the provider network 2000; a customer may then assign theirallocated public IP addresses to particular resource instances allocatedto the customer. These public IP addresses may be referred to ascustomer public IP addresses, or simply customer IP addresses. Insteadof being assigned by the provider network 2000 to resource instances asin the case of standard IP addresses, customer IP addresses may beassigned to resource instances by the customers, for example via an APIprovided by the service provider. Unlike standard IP addresses, customerIP addresses are allocated to customer accounts and can be remapped toother resource instances by the respective customers as necessary ordesired. A customer IP address is associated with a customer's account,not a particular resource instance, and the customer controls that IPaddress until the customer chooses to release it. Unlike conventionalstatic IP addresses, customer IP addresses allow the customer to maskresource instance or availability zone failures by remapping thecustomer's public IP addresses to any resource instance associated withthe customer's account. The customer IP addresses, for example, enable acustomer to engineer around problems with the customer's resourceinstances or software by remapping customer IP addresses to replacementresource instances.

FIG. 21 is a block diagram of an example provider network that providesa storage service and a hardware virtualization service to customers,according to some embodiments. Hardware virtualization service 2120provides multiple computation resources 2124 (e.g., VMs) to customers.The computation resources 2124 may, for example, be rented or leased tocustomers of the provider network 2100 (e.g., to a customer thatimplements customer network 2150). Each computation resource 2124 may beprovided with one or more local IP addresses. Provider network 2100 maybe configured to route packets from the local IP addresses of thecomputation resources 2124 to public Internet destinations, and frompublic Internet sources to the local IP addresses of computationresources 2124.

Provider network 2100 may provide a customer network 2150, for examplecoupled to intermediate network 2140 via local network 2156, the abilityto implement virtual computing systems 2192 via hardware virtualizationservice 2120 coupled to intermediate network 2140 and to providernetwork 2100. In some embodiments, hardware virtualization service 2120may provide one or more APIs 2102, for example a web services interface,via which a customer network 2150 may access functionality provided bythe hardware virtualization service 2120, for example via a console 2194(e.g., a web-based application, standalone application, mobileapplication, etc.). In some embodiments, at the provider network 2100,each virtual computing system 2192 at customer network 2150 maycorrespond to a computation resource 2124 that is leased, rented, orotherwise provided to customer network 2150.

From an instance of a virtual computing system 2192 and/or anothercustomer device 2190 (e.g., via console 2194), the customer may accessthe functionality of storage service 2110, for example via one or moreAPIs 2102, to access data from and store data to storage resources2118A-2118N of a virtual data store 2116 (e.g., a folder or “bucket”, avirtualized volume, a database, etc.) provided by the provider network2100. In some embodiments, a virtualized data store gateway (not shown)may be provided at the customer network 2150 that may locally cache atleast some data, for example frequently-accessed or critical data, andthat may communicate with storage service 2110 via one or morecommunications channels to upload new or modified data from a localcache so that the primary store of data (virtualized data store 2116) ismaintained. In some embodiments, a user, via a virtual computing system2192 and/or on another customer device 2190, may mount and accessvirtual data store 2116 volumes via storage service 2110 acting as astorage virtualization service, and these volumes may appear to the useras local (virtualized) storage 2198.

While not shown in FIG. 21, the virtualization service(s) may also beaccessed from resource instances within the provider network 2100 viaAPI(s) 2102. For example, a customer, appliance service provider, orother entity may access a virtualization service from within arespective virtual network on the provider network 2100 via an API 2102to request allocation of one or more resource instances within thevirtual network or within another virtual network.

Illustrative Systems

In some embodiments, a system that implements a portion or all of thetechniques described herein may include a general-purpose computersystem that includes or is configured to access one or morecomputer-accessible media, such as computer system 2200 illustrated inFIG. 22. In the illustrated embodiment, computer system 2200 includesone or more processors 2210 coupled to a system memory 2220 via aninput/output (I/O) interface 2230. Computer system 2200 further includesa network interface 2240 coupled to I/O interface 2230. While FIG. 22shows computer system 2200 as a single computing device, in variousembodiments a computer system 2200 may include one computing device orany number of computing devices configured to work together as a singlecomputer system 2200.

In various embodiments, computer system 2200 may be a uniprocessorsystem including one processor 2210, or a multiprocessor systemincluding several processors 2210 (e.g., two, four, eight, or anothersuitable number). Processors 2210 may be any suitable processors capableof executing instructions. For example, in various embodiments,processors 2210 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, ARM, PowerPC, SPARC, or MIPS ISAs, or any othersuitable ISA. In multiprocessor systems, each of processors 2210 maycommonly, but not necessarily, implement the same ISA.

System memory 2220 may store instructions and data accessible byprocessor(s) 2210. In various embodiments, system memory 2220 may beimplemented using any suitable memory technology, such as random-accessmemory (RAM), static RAM (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/Flash-type memory, or any other type of memory. In theillustrated embodiment, program instructions and data implementing oneor more desired functions, such as those methods, techniques, and datadescribed above are shown stored within system memory 2220 as enterprisesearch service code 2225 and data 2226.

In one embodiment, I/O interface 2230 may be configured to coordinateI/O traffic between processor 2210, system memory 2220, and anyperipheral devices in the device, including network interface 2240 orother peripheral interfaces. In some embodiments, I/O interface 2230 mayperform any necessary protocol, timing or other data transformations toconvert data signals from one component (e.g., system memory 2220) intoa format suitable for use by another component (e.g., processor 2210).In some embodiments, I/O interface 2230 may include support for devicesattached through various types of peripheral buses, such as a variant ofthe Peripheral Component Interconnect (PCI) bus standard or theUniversal Serial Bus (USB) standard, for example. In some embodiments,the function of I/O interface 2230 may be split into two or moreseparate components, such as a north bridge and a south bridge, forexample. Also, in some embodiments some or all of the functionality ofI/O interface 2230, such as an interface to system memory 2220, may beincorporated directly into processor 2210.

Network interface 2240 may be configured to allow data to be exchangedbetween computer system 2200 and other devices 2260 attached to anetwork or networks 2250, such as other computer systems or devices asillustrated in FIG. 1, for example. In various embodiments, networkinterface 2240 may support communication via any suitable wired orwireless general data networks, such as types of Ethernet network, forexample. Additionally, network interface 2240 may support communicationvia telecommunications/telephony networks such as analog voice networksor digital fiber communications networks, via storage area networks(SANs) such as Fibre Channel SANs, or via I/O any other suitable type ofnetwork and/or protocol.

In some embodiments, a computer system 2200 includes one or more offloadcards 2270 (including one or more processors 2275, and possiblyincluding the one or more network interfaces 2240) that are connectedusing an I/O interface 2230 (e.g., a bus implementing a version of thePeripheral Component Interconnect-Express (PCI-E) standard, or anotherinterconnect such as a QuickPath interconnect (QPI) or UltraPathinterconnect (UPI)). For example, in some embodiments the computersystem 2200 may act as a host electronic device (e.g., operating as partof a hardware virtualization service) that hosts compute instances, andthe one or more offload cards 2270 execute a virtualization manager thatcan manage compute instances that execute on the host electronic device.As an example, in some embodiments the offload card(s) 2270 can performcompute instance management operations such as pausing and/or un-pausingcompute instances, launching and/or terminating compute instances,performing memory transfer/copying operations, etc. These managementoperations may, in some embodiments, be performed by the offload card(s)2270 in coordination with a hypervisor (e.g., upon a request from ahypervisor) that is executed by the other processors 2210A-2210N of thecomputer system 2200. However, in some embodiments the virtualizationmanager implemented by the offload card(s) 2270 can accommodate requestsfrom other entities (e.g., from compute instances themselves), and maynot coordinate with (or service) any separate hypervisor.

In some embodiments, system memory 2220 may be one embodiment of acomputer-accessible medium configured to store program instructions anddata as described above. However, in other embodiments, programinstructions and/or data may be received, sent or stored upon differenttypes of computer-accessible media. Generally speaking, acomputer-accessible medium may include non-transitory storage media ormemory media such as magnetic or optical media, e.g., disk or DVD/CDcoupled to computer system 2200 via I/O interface 2230. A non-transitorycomputer-accessible storage medium may also include any volatile ornon-volatile media such as RAM (e.g., SDRAM, double data rate (DDR)SDRAM, SRAM, etc.), read only memory (ROM), etc., that may be includedin some embodiments of computer system 2200 as system memory 2220 oranother type of memory. Further, a computer-accessible medium mayinclude transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network and/or a wireless link, such as may be implemented vianetwork interface 2240.

Various embodiments discussed or suggested herein can be implemented ina wide variety of operating environments, which in some cases caninclude one or more user computers, computing devices, or processingdevices which can be used to operate any of a number of applications.User or client devices can include any of a number of general-purposepersonal computers, such as desktop or laptop computers running astandard operating system, as well as cellular, wireless, and handhelddevices running mobile software and capable of supporting a number ofnetworking and messaging protocols. Such a system also can include anumber of workstations running any of a variety of commerciallyavailable operating systems and other known applications for purposessuch as development and database management. These devices also caninclude other electronic devices, such as dummy terminals, thin-clients,gaming systems, and/or other devices capable of communicating via anetwork.

Most embodiments utilize at least one network that would be familiar tothose skilled in the art for supporting communications using any of avariety of widely-available protocols, such as Transmission ControlProtocol/Internet Protocol (TCP/IP), File Transfer Protocol (FTP),Universal Plug and Play (UPnP), Network File System (NFS), CommonInternet File System (CIFS), Extensible Messaging and Presence Protocol(XMPP), AppleTalk, etc. The network(s) can include, for example, a localarea network (LAN), a wide-area network (WAN), a virtual private network(VPN), the Internet, an intranet, an extranet, a public switchedtelephone network (PSTN), an infrared network, a wireless network, andany combination thereof.

In embodiments utilizing a web server, the web server can run any of avariety of server or mid-tier applications, including HTTP servers, FileTransfer Protocol (FTP) servers, Common Gateway Interface (CGI) servers,data servers, Java servers, business application servers, etc. Theserver(s) also may be capable of executing programs or scripts inresponse requests from user devices, such as by executing one or moreWeb applications that may be implemented as one or more scripts orprograms written in any programming language, such as Java®, C, C# orC++, or any scripting language, such as Perl, Python, PHP, or TCL, aswell as combinations thereof. The server(s) may also include databaseservers, including without limitation those commercially available fromOracle®, Microsoft®, Sybase®, IBM®, etc. The database servers may berelational or non-relational (e.g., “NoSQL”), distributed ornon-distributed, etc.

Environments disclosed herein can include a variety of data stores andother memory and storage media as discussed above. These can reside in avariety of locations, such as on a storage medium local to (and/orresident in) one or more of the computers or remote from any or all ofthe computers across the network. In a particular set of embodiments,the information may reside in a storage-area network (SAN) familiar tothose skilled in the art. Similarly, any necessary files for performingthe functions attributed to the computers, servers, or other networkdevices may be stored locally and/or remotely, as appropriate. Where asystem includes computerized devices, each such device can includehardware elements that may be electrically coupled via a bus, theelements including, for example, at least one central processing unit(CPU), at least one input device (e.g., a mouse, keyboard, controller,touch screen, or keypad), and/or at least one output device (e.g., adisplay device, printer, or speaker). Such a system may also include oneor more storage devices, such as disk drives, optical storage devices,and solid-state storage devices such as random-access memory (RAM) orread-only memory (ROM), as well as removable media devices, memorycards, flash cards, etc.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.), and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed, and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting, and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services, or other elementslocated within at least one working memory device, including anoperating system and application programs, such as a client applicationor web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets), or both. Further, connection to other computing devicessuch as network input/output devices may be employed.

Storage media and computer readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer readable instructions, data structures,program modules, or other data, including RAM, ROM, ElectricallyErasable Programmable Read-Only Memory (EEPROM), flash memory or othermemory technology, Compact Disc-Read Only Memory (CD-ROM), DigitalVersatile Disk (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by a system device. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

In the preceding description, various embodiments are described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described.

Bracketed text and blocks with dashed borders (e.g., large dashes, smalldashes, dot-dash, and dots) are used herein to illustrate optionaloperations that add additional features to some embodiments. However,such notation should not be taken to mean that these are the onlyoptions or optional operations, and/or that blocks with solid bordersare not optional in certain embodiments.

Reference numerals with suffix letters may be used to indicate thatthere can be one or multiple instances of the referenced entity invarious embodiments, and when there are multiple instances, each doesnot need to be identical but may instead share some general traits oract in common ways. Further, the particular suffixes used are not meantto imply that a particular amount of the entity exists unlessspecifically indicated to the contrary. Thus, two entities using thesame or different suffix letters may or may not have the same number ofinstances in various embodiments.

References to “one embodiment,” “an embodiment,” “an exampleembodiment,” etc., indicate that the embodiment described may include aparticular feature, structure, or characteristic, but every embodimentmay not necessarily include the particular feature, structure, orcharacteristic. Moreover, such phrases are not necessarily referring tothe same embodiment. Further, when a particular feature, structure, orcharacteristic is described in connection with an embodiment, it issubmitted that it is within the knowledge of one skilled in the art toaffect such feature, structure, or characteristic in connection withother embodiments whether or not explicitly described.

Moreover, in the various embodiments described above, unlessspecifically noted otherwise, disjunctive language such as the phrase“at least one of A, B, or C” is intended to be understood to mean eitherA, B, or C, or any combination thereof (e.g., A, B, and/or C). As such,disjunctive language is not intended to, nor should it be understood to,imply that a given embodiment requires at least one of A, at least oneof B, or at least one of C to each be present.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the disclosure asset forth in the claims.

What is claimed is:
 1. A computer-implemented method comprising:receiving a document search query at an enterprise search service;querying a set of one or more indexes based upon the document searchquery to identify matching data, the matching data comprising a set ofdocuments, a set of passages, and a set of frequently asked questions,each frequently asked question of the set of frequently asked questionscomprising a respective question and answer pair; ranking the set ofdocuments using a first machine learning model to determine a set of oneor more best matching, documents; determining a set of one or more bestmatching passages from the set of passages using a second machinelearning model; determining a best matching passage from the set of bestmatching passages using a third machine learning model; determining abest matching frequently asked question of the set of frequently askedquestions using a fourth machine learning model; and displaying ananswer to the document search query in a graphical user interface, theanswer comprising the best matching passage, the best matchingdocuments, and the best matching frequently asked question.
 2. Thecomputer-implemented method of claim 1, wherein the first machinelearning model is a deep cross network model and the second, third, andfourth machine learning models are each a bidirectional encoderrepresentations from transformers model.
 3. The computer-implementedmethod of claim 1, further comprising: receiving feedback on the bestmatching passage, the best matching documents, and the best matchingfrequently asked question.
 4. A computer-implemented method comprising:receiving a document search query at an enterprise search service;querying a set of one or more indexes based upon the document searchquery to identify matching data, the matching data comprising a set ofdocuments, a set of passages, and a set of frequently asked questions;using a machine learning model to determine a best matching passage forthe document search query, the best matching passage being in the set ofpassages; using a machine learning model to determine a best matchingfrequently asked question for the document search query, the bestmatching frequently asked being in the set of frequently askedquestions; and causing display of a graphical user interface thatprovides an answer to the document search query, the answer comprisingthe best matching passage, the best matching frequently asked question,and a document of the set of documents.
 5. The computer-implementedmethod of claim 4, further comprising: using a machine learning model torank the set of documents and to determine a set of one or more bestmatching documents; and wherein the document of the set of documents ofthe answer is in the set of one or more best matching documents.
 6. Thecomputer-implemented method of claim 5, wherein the machine learningmodel used to rank the set of documents and to determine the set of bestmatching documents is a deep cross network model and the machinelearning model used to determine the best matching passage is abidirectional encoder representations from transformers model.
 7. Thecomputer-implemented method of claim 4, the best matching frequentlyasked question comprises a respective question and a respective answer;and wherein the graphical user interface presents the respectivequestion and the respective answer.
 8. The computer-implemented methodof claim 4, wherein the one or more indexes comprises a plurality ofindexes; wherein the plurality of indexes comprise a first index and asecond index; wherein the first index indexes the set of documents;wherein the second index indexes the set of frequently asked questions;and wherein the querying the one or more indexes comprises querying thefirst index to identify the set of documents and querying the secondindex to identify the set of frequently asked questions.
 9. Thecomputer-implemented method of claim 4, wherein the document searchquery includes a natural language question.
 10. The computer-implementedmethod of claim 4, wherein a text portion of best matching passage isvisually highlighted in the graphical user interface with a type ofvisual text emphasis; and wherein the type of visual text emphasis isselected based on how relevant the best matching passage is to thedocument search query.
 11. The computer-implemented method of claim 4,wherein the set of documents comprise word processing documents, textfiles, and postscript-based files.
 12. The computer-implemented methodof claim 4, further comprising: ingesting the set of documents into theenterprise search service from a data source that is external to aprovider network, the provider network comprising the enterprise searchservice.
 13. The computer-implemented method of claim 4, furthercomprising: ingesting the set of frequently asked questions into theenterprise search service from a data source; and wherein eachfrequently asked question of the set of frequently asked questionscomprises a respective question and answer pair.
 14. Thecomputer-implemented method of claim 4, further comprising: receivingfeedback on the best matching passage; and updating the machine learningmodel used to determine the best matching passage based on the feedback.15. A system comprising: data storage to store a set of documents; andone or more electronic devices to implement an enterprise searchservice, the enterprise search service including instructions that uponexecution cause the enterprise search service to: receive a documentsearch query, query one or more indexes based upon the document searchquery to identify matching data, the matching data to comprise a set ofdocuments, a set of passages, and a set of frequently asked questions,use a machine learning model to determine a best matching passage forthe document search query, the best matching passage to be in the set ofpassages, use a machine learning model to determine a best matchingfrequently asked question for the document search query, the bestmatching frequently asked question to be in the set of frequently askedquestions, and cause display of a graphical user interface that providesan answer to the document search query, the answer to comprise the bestmatching passage, the best matching frequently asked question, and adocument of the set of documents.
 16. The system of claim 15, whereinthe enterprise search service further includes instructions that uponexecution cause the enterprise search service to: use a machine learningmodel to rank the set of documents and to determine a set of one or morebest matching documents; and wherein the document of the set ofdocuments of the answer is to be in the set of one or more best matchingdocuments.
 17. The system of claim 15, wherein the machine learningmodel to use to rank the set of documents and to determine the set ofbest matching documents is to be a deep cross network model and themachine learning model to use to determine the best matching passage isto be a bidirectional encoder representations from transformers model.18. The system of claim 15, wherein the document search query includes anatural language question.
 19. The system of claim 15, wherein the setof documents comprise word processing documents, text files, andpostscript-based files.
 20. The system of claim 15, wherein a textportion of best matching passage is to be visually highlighted in thegraphical user interface with a type of visual text emphasis; andwherein the type of visual text emphasis is to be selected based on howrelevant the best matching passage is to the document search query.